IDES GIS Workshop Day 4

Professor Dawn Wright

Grad Assistant Lalo Guerrero

Feature Type! !Single Part ! !Multi-Part!

Point!!Line !!!

Area!!Annotation!!!!

W A Y 10

Object/Vector Feature Types

Raster or Grid Data Structure. R o w s

Columns

Grid!cell!

Grid extent!

Resolution!

Feature Type! !Single Part ! !Multi-Part!

Point!!Line !!!

Area!!Annotation!!!!

W A Y 10

Object/Vector Feature Types

“Rasters tell WHAT occurs everywhere.”

!  natural for scanned or remotely sensed data.

!  continuous surfaces (e.g., topography) !  spatial analytical operations are faster. !  compression is easier

Orange County, CA

Courtesy of Russ Michel, Pictometry Intl. Inc.

Raster

Vector

Projected with flat ground plane Projected with tessellated ground plane

Courtesy of Russ Michel, Pictometry Intl. Inc.

Orange County Street Centerlines

National Polar-orbiting Operational Environment Satellite System (NPOESS), a next-generation platform for weather and climate.

Courtesy of Ted Strub, OSU CIOSS

Components of a passive remote sensing system Example with infrared or microwave wavelengths

signal raw data

processing / dissemination calibration/

validation

sensor

IR or µ-wave Emission

Two types of remote sensing:

Passive

Courtesy of Ted Strub, OSU CIOSS

LandSat is an example of a passive remote sensing system (spaceborne satellite)

IR or µ-wave Emission

Old MSS – Multispectral

Scanner

Current ETM –Enhanced

Thematic Mapper

Components of an active remote sensing system Transmit at nadir (directly beneath satellite)

source and sensor!

signal raw data

processing / dissemination calibration/

validation

Two types of remote sensing:

Active

µ-wave radar Reflection

Courtesy of Ted Strub, OSU CIOSS

LIDAR is an example of an active remote sensing system (airborne)

IR or µ-wave Emission

A. Nayegandhi, USGS

Aircraft elevation ~700 m

Scan width ~300 m

Scan angle ~30°